Nonlinear Estimation with State-Dependent Gaussian Observation Noise

dc.contributor.author	Spinello, D.	en
dc.contributor.author	Stilwell, Daniel J.	en
dc.contributor.department	Virginia Center for Autonomous Systems	en
dc.date.accessed	2013-04-25	en
dc.date.accessioned	2013-04-25T20:55:52Z	en
dc.date.available	2013-04-25T20:55:52Z	en
dc.date.issued	2008	en
dc.description	24 p.	en
dc.description.abstract	We consider the problem of estimating the state of a system when measurement noise is a function of the system's state. We propose generalizations of the iterated extended Kalman filter and of the extended Kalman filter that can be utilized when the state estimate distribution is approximately Gaussian. The state estimate is computed by an iterative root-searching method that maximize a maximum likelihood function. For sensor network applications, we also address distributed implementations involving multiple sensors.	en
dc.format.mimetype	application/pdf	en
dc.identifier.uri	http://hdl.handle.net/10919/19356	en
dc.identifier.url	http://www.unmanned.vt.edu/discovery/reports/VaCAS_2008_02.pdf	en
dc.language	English	en
dc.publisher	Virginia Center for Autonomous Systems	en
dc.relation.ispartofseries	VaCAS	en
dc.rights	In Copyright	en
dc.rights.holder	Copyright, Virginia Polytechnic Institute and State University	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Kalman filtering	en
dc.subject	Sensor networks	en
dc.title	Nonlinear Estimation with State-Dependent Gaussian Observation Noise	en
dc.title.alternative	VaCAS-2008-02	en
dc.type	Technical report	en
dc.type.dcmitype	Text	en

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